This tool is used for dumping out the rccl-tests/nccl-test commands directly from an application to identify any potential bottlenecks of scaling while using RCCL/NCCL modules when running a distributed applications.

To get started please clone the following repository: git clone --recursive https://github.com/lcskrishna/nccl-rccl-parser.git

To run the tests, we use the following repositories:

• On ROCm: https://github.com/ROCmSoftwarePlatform/rccl-tests
• On CUDA: https://github.com/NVIDIA/nccl-tests.git

• RCCL/NCCL installed.
• rccl-tests or nccl-tests installed.

Firstly, make sure you are running the experiments of a distributed setup of an application. Make sure to run the application for at least 1 iteration using the below two environment variables into a log file named nccl_debug_log.txt

NCCL_DEBUG=INFO NCCL_DEBUG_SUBSYS=INIT,COLL <application> |& tee nccl_debug_log.txt

To gather the performance results once you have the debug log with you. Run the below command.

On CUDA devices, use --cuda argument.

On ROCm devices, use --rocm argument.

Note: If you don't mention the arguments the automated script only dumps out the output data from the parser.

On ROCm:

python run_parser_and_generate_summary.py --nccl-debug-log nccl_debug_log.txt --rocm

On CUDA:

python run_parser_and_generate_summary.py --nccl-debug-log nccl_debug_log.txt --cuda

Use Parser to dump out the test commands:

Once the log is being collected, use the parser to dump out all the rccl/nccl test commands or just the unique commands with their respective counts of the workload. Note: To dump out the unique commands use the --unique argument. Optional parameters: output-script-name, unique

Here is the usage of the script

python rccl_nccl_parser.py --nccl-debug-log nccl_debug_log.txt --output-script-name net
(or)
python rccl_nccl_parser.py --nccl-debug-log nccl_debug_log.txt --output-script-name net --unique

The first command dumps out all the rccl/nccl tests in the order they get executed in the application. (net_rccl_nccl.sh file). The second command dumps out a script file with unique commands and a csv file with commands and its counts of each command.

Run rccl-tests/nccl-tests:

Once you dump out the scripts, make sure to copy the script in nccl-tests/rccl-tests folder and run the script and gather the Inside nccl-tests/rccl-tests repository:

sh net_unique.sh |& tee rccl_perf_data.txt

Once you run the above script, the performance data of each command is redirected to a text file.

Generate Summary:

Now the final step is to use the above performance log and generate a summary in the form of CSV file for each of the command. The command gives the average values for each command like Time(us), algBw, busBw (out-of-place and in-place). For pytorch please consider out of place options.

To generate the summary, navigate to the tool nccl-rccl-parser:

python generate_summary.py --log-file rccl_perf_data.txt --output-file-name test_app_data--script-file net_unique.sh 

This dumps out a csv file with performance data for further analysis.

Supported Collectives:

Currently only the AllReduce and Broadcast calls are being supported by this tool. Based on running more experiments other collectives need to be added.